Functional characterisation of human microbiomes using metatranscriptomics
Bibliographic record
Abstract
The complex assemblages of microbiota that colonise mucosal surfaces of the human body can change throughout our lives, such as during the initial succession period after birth, during disease, and during pregnancy. Over the past two decades, these microbiomes have been intimately linked to human health. While the exact mechanisms are not well-understood, dysbiosis (a change in diversity or composition) of the microbiome have been implicated in a range of diseases and disorders. Many past studies have been reliant on amplicon marker surveys of the 16S ribosomal RNA genes for community profiling. In contrast, whole microbiome RNA-sequencing or “metatranscriptomics” allows for elucidation the functions of microorganisms within the community. The purpose of my thesis is to investigate how microbial functional profiles are influenced by disease states and pregnancy using metatranscriptomics and state-of-the-art computational tools. I leverage two datasets to develop a comprehensive workflow for characterising the functional profile of complex microbial communities: samples from a study investigating the impact of influenza on the upper respiratory tract microbiome and samples from a study investigating the gut microbiome during pregnancy.With the influenza dataset, I identify peptidoglycan biosynthesis and cysteine and methione metabolism to be over-represented in genes that are differentially expressed in samples from households where influenza transmission occurred. Expression of genes related to iron scavenging, antibiotic resistance, and biofilm synthesis were not significantly impacted by influenza. But consistent expression of the Pel operon was observed across the samples. With the pregnancy dataset, I characterise a large cohort of mothers and infants based on anthropometric and nutrition-related metrics. I use a pilot study of metatranscriptomic samples to identify increased expression of enzymes involved in Energy and Carbohydrate metabolism super pathways in the third trimester compared to the first trimester of pregnancy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".